Build, test, and deploy quantitative strategies with real market data. Factor models, portfolio optimization, Python sandbox — all in one platform.
Most investors make portfolio decisions based on news, gut feel, or tips. Systematic investing is the opposite: you define rules, test them on historical data, and follow the system — not emotions.
Hedge funds and large asset managers have done this for decades using factor models — mathematical frameworks that explain why stocks go up or down (size, momentum, value, quality). Until now, these tools were inaccessible without institutional infrastructure.
Galedge brings that infrastructure to individual researchers, students, and portfolio managers. You define the strategy. The platform handles the math.
Not dashboards. Not charts for the sake of charts. Every feature answers a specific question a portfolio manager or researcher would actually ask.
Brinson attribution, factor decomposition, peer comparison — see which decisions added alpha and which didn't.
Backtest any strategy over real NSE price history. See the equity curve, drawdowns, and Sharpe before committing capital.
21-factor risk model reveals your true exposures — market beta, size, momentum, value, and 10 industry tilts.
Set position limits, beta bounds, sector caps. The optimizer finds the best allocation within your rules — not just the textbook answer.
Full VS Code IDE with Python, pandas, and live market data. Isolated sandbox per user — no setup, no cloud bills.
Promote a strategy to production. Get exact BUY/SELL quantities at current prices, updated at each rebalance.
Combine VALUE, PROFIT, MOMENTUM factors into a scoring model. Compute IC, IR, and t-stats to know if your signal is real — or noise.
Filter stocks with a screener, rank survivors with alpha scores, weight by signal strength, backtest with real transaction costs.
Excel is great for simple models. It breaks down the moment you need live data, optimization, or proper attribution.
Four steps. No infrastructure to set up.
Upload a CSV with your holdings. Market data is fetched automatically in the background.
Run factor attribution, compare against benchmark, write custom Python research in the code editor.
Set constraints and objectives, run the optimizer, see the backtest equity curve with transaction costs.
Promote to production. Generate rebalance trade lists with exact quantities and current prices.
Free to start. Upload your portfolio, run analytics, backtest strategies. Go live when you're ready.